Surrogate models and active learning for scientific applications.
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Updated
Jul 17, 2024 - R
Surrogate models and active learning for scientific applications.
Surrogate Modeling Toolbox
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods
core C++ library
A GNN-based surrogate model of urban drainage networks.
Surrogate modeling and optimization for scientific machine learning (SciML)
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
R interface to 'dgpsi' for deep and linked Gaussian process emulations
Dimension reduced surrogate construction for parametric PDE maps
Multi-Fidelity Bayesian Optimization Tutorial
A python package for surrogate models that interface with calibration and other tools
Numerical Evidence for Sample Efficiency of Model-Based over Model-Free Reinforcement Learning Control of Partial Differential Equations [ECC'24]
An easy to use interface to gravitational wave surrogate models
Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
Python package 'dgpsi' for deep and linked Gaussian process emulations
Neural architecture search for object detectors using non dominated sorting genetic algorithm and surrogate optimization
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